Is there a way to test out the pretrained image classification models released by Google called 'MobileNets' using only the Keras API?
Models like ResNet50 and InceptionV3 are already available as Keras Applications, but I couldn't find documentation on using custom tensorflow models with Keras. Thanks in advance.
AFAIK, there is no direct access via Keras API to it. However, you can find a good interaction of Keras and MobileNets
here.
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I am trying to experiment with Keras, and thought I would create my own layer. However, I am using an AMD GPU (PlaidML) without Tensorflow. I have seen some tutorials online, but they all require Tensorflow. How would I create a custom Keras layer without using Tensorflow?
The documentation for porting an already trained TensorFlow Model to iOS is well defined:
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/ios
However, nowhere is mentioned if the model:
can be further trained on the device, or
can be created from scratch and trained on the device
Is this possible with TensorFlow?
I am aware of other Swift/C++ libraries that offer on-device training, but I am more interested in this technology.
Starting with CoreML3 and the UpdatableTask, the training on device is now part of the API: https://developer.apple.com/documentation/coreml/mlupdatetask
I have just started exploring CoreML and was wondering if there is a way to train a Binary Classification Model using the same.
Please provide me any references or examples as I am a ML noob.
Core ML doesn't offer any APIs for building or training models. It works with models you've already trained elsewhere (Keras, Caffe, etc) to perform whatever prediction or classification task you built the model for. See Apple's Core ML docs for info on how to convert a model for use with Core ML.
Core ML offers building and training models as of macOS 10.14 (Mojave). In XCode you can train models in various ways.
I don't believe they currently support a binary classifier, but if you can build an image set of X and NOT X you could emulate such.
Apple's Docs: https://developer.apple.com/documentation/create_ml/creating_an_image_classifier_model
I have successfully used LeNet model to train my own dataset using Siamese Network using this tutorial. Now I want to use AlexNet as I believe it is more powerful than LeNet. Can someone provide guidelines or tutorial to use the AlexNet in Siamese Network.
You should go through this github repository. Specially the models and examples section. You can get the implementation of AlexNet in caffe here.
N.B. Please don't flag this post because of its length or sharing link. Links that I shared in this answer contains large code which I cannot post as an answer.
After training lenet model in caffe framework using 10k images,i got the model lenet_iter_4000.caffemodel which contains weights and baises. I did in caffe for predicting test image classification, Now i wanted to do classification in OpenCV by loading this caffemodel for test image, Can anybody please help me how to combine caffe and OpenCV for predicting new image..
OpenCV contrib contains a module called dnn that can be used for this, it can load Caffe and Torch models, and here is a tutorial for GoogleNet, you can easily adapt it to use another network, the code is basically the same.
An alternative is the classification.cpp example in Cafee's source, which uses OpenCV to read an image and process it with Caffe.